In this video from the HPC User Forum, Frank Alexander from Brookhaven National Laboratory presents: ExaLearn – ECP Co-Design Center for Machine Learning. “It is increasingly clear that advances in learning technologies have profound societal implications and that continued U.S. economic leadership requires a focused effort, both to increase the performance of those technologies and to expand their applications. Linking exascale computing and learning technologies represents a timely opportunity to address those goals.”

Mike Heroux from Sandia National Labs gave this talk at the HPC User Forum. “The Exascale Computing Project is accelerating delivery of a capable exascale computing ecosystem for breakthroughs in scientific discovery, energy assurance, economic competitiveness, and national security.The goal of the ECP Software Technology focus area is to develop a comprehensive and coherent software stack that will enable application developers to productively write highly parallel applications that can portably target diverse exascale architectures.”

In this podcast, Franck Cappello from Argonne describes EZ, an effort to effort to compress and reduce the enormous scientific data sets that some of the ECP applications are producing. “There are different approaches to solving the problem. One is called lossless compression, a data-reduction technique that doesn’t lose any information or introduce any noise. The drawback with lossless compression, however, is that user-entry floating-point values are very difficult to compress: the best effort reduces data by a factor of two. In contrast, ECP applications seek a data reduction factor of 10, 30, or even more.”

As supercomputers become ever more capable in their march toward exascale levels of performance, scientists can run increasingly detailed and accurate simulations to study problems ranging from cleaner combustion to the nature of the universe. Enter ExaLearn, a new machine learning project supported by DOE’s Exascale Computing Project (ECP), aims to develop new tools to help scientists overcome this challenge by applying machine learning to very large experimental datasets and simulations.

Computationally, EXAALT’s goal is to develop a comprehensive molecular dynamics capability for exascale. “The user should be able to say, ‘I’m interested in this kind of system size, timescale, and accuracy,’ and directly access the regime without being constrained by the usual scaling paths of current codes,” said Danny Perez of Los Alamos National Laboratory (LANL) and the EXAALT team.

In this podcast, Doug Kothe from the Exascale Computing Project describes the 2019 ECP Annual Meeting. “Key topics to be covered at the meeting are discussions of future systems, software stack plans, and interactions with facilities. Several parallel sessions are also planned throughout the meeting.”

Researchers are looking to HPC to help engineer cost-effective carbon capture and storage technologies for tomorrow’s power plants. “By combining new algorithmic approaches and a new software infrastructure, MFiX-Exa will leverage future exascale machines to optimize CLRs. Exascale will provide 50 times more computational science and data analytic application power than is possible with DOE high-performance computing systems such as Titan at the Oak Ridge Leadership Computing Facility (OLCF) and Sequoia at Lawrence Livermore National Laboratory.”

Scientists and Engineers at Berkeley Lab are busy preparing for Exascale supercomputing this week at the ECP Annual Meeting in Houston. With a full agenda running five days, LBL researchers will contribute Two Plenaries, Five Tutorials, 15 Breakouts and 20 Posters. “Sponsored by the Exascale Computing Project, the ECP Annual Meeting centers around the many technical accomplishments of our talented research teams, while providing a collaborative working forum that includes featured speakers, workshops, tutorials, and numerous planning and co-design meetings in support of integrated project understanding, team building and continued progress.”

In this video from the Exascale Computing Project, Dave Montoya from LANL describes the continuous software integration effort at DOE facilities where exascale computers will be located sometime in the next 3-4 years. “A key aspect of the Exascale Computing Project’s continuous integration activities is ensuring that the software in development for exascale can efficiently be deployed at the facilities and that it properly blends with the facilities’ many software components. As is commonly understood in the realm of high-performance computing, integration is very challenging: both the hardware and software are complex, with a huge amount of dependencies, and creating the associated essential healthy software ecosystem requires abundant testing.”

In the podcast, Mike Bernhardt from ECP catches up with Sameer Shende to learn how the Performance Research Lab at the University of Oregon is helping to pave the way to Exascale. “Developers of parallel computing applications can well appreciate the Tuning and Analysis Utilities (TAU) performance evaluation tool—it helps them optimize their efforts. Sameer has worked with the TAU software for nearly two and a half decades and has released more than 200 versions of it. Whatever your application looks like, there’s a good chance that TAU can support it and help you improve your performance.”

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